{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "14c11c13-4fef-48c4-b2c6-9cb80114d7ae",
   "metadata": {},
   "source": [
    "### 数据读取"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "122dee9d-bacc-474e-be0d-e0cda205a081",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from torchvision import transforms, datasets\n",
    "import os\n",
    "import torch\n",
    "from torch.utils.data import DataLoader\n",
    "from tqdm import tqdm\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "dfc87e00-5f9e-47a0-8d44-cea4adf12b4b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "device(type='cuda')"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 检查是否有可用的 GPU\n",
    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "device"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "610cf08d-474d-495f-b1ee-c88bad404d90",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Dataset ImageFolder\n",
       "    Number of datapoints: 22500\n",
       "    Root location: ../datasets/PetImages\\train\n",
       "    StandardTransform\n",
       "Transform: Compose(\n",
       "               RandomHorizontalFlip(p=0.5)\n",
       "               RandomRotation(degrees=[-15.0, 15.0], interpolation=nearest, expand=False, fill=0)\n",
       "               ColorJitter(brightness=(0.99, 1.01), contrast=(0.99, 1.01), saturation=(0.99, 1.01), hue=(-0.01, 0.01))\n",
       "               RandomResizedCrop(size=(150, 150), scale=(0.8, 1.0), ratio=(0.75, 1.3333), interpolation=bilinear, antialias=True)\n",
       "               ToTensor()\n",
       "               Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])\n",
       "           )"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 定义数据增强和预处理的 transform  数据集是彩色图片, 有三个channel\n",
    "\"\"\"\n",
    "数据增强\n",
    "水平翻转：模拟左右对称场景（如人脸、物体的翻转），增强数据多样性。\n",
    "旋转：小角度旋转模拟不同视角下的图像，提高模型的鲁棒性。\n",
    "颜色抖动：通过改变亮度、对比度、饱和度和色调，模拟光线条件的变化，提升模型在复杂光线下的适应性。\n",
    "随机裁剪：随机从原图中裁剪区域，这对定位鲁棒性较差的模型非常有帮助。\n",
    "张量化：将图像数据从像素值转换为适合模型处理的格式。\n",
    "\"\"\"\n",
    "transform = transforms.Compose([\n",
    "    transforms.RandomHorizontalFlip(),  # 随机水平翻转，增强样本的多样性，概率为 50%\n",
    "    transforms.RandomRotation(15),  # 随机旋转图像，角度范围为 -15° 到 15°\n",
    "    transforms.ColorJitter(          # 随机调整图像的颜色属性：\n",
    "        brightness=0.01,             # 调整亮度，范围为 [0.8, 1.2]（1±0.2）\n",
    "        contrast=0.01,               # 调整对比度，范围为 [0.8, 1.2]\n",
    "        saturation=0.01,             # 调整饱和度，范围为 [0.8, 1.2]\n",
    "        hue=0.01                     # 调整色调，范围为 [-0.1, 0.1]\n",
    "    ),\n",
    "    transforms.RandomResizedCrop(150, scale=(0.8, 1.0)),  # 把图片的每一个channel随机裁剪成 150x150, 裁剪比例范围为 80% - 100%\n",
    "    transforms.ToTensor(),             # 将图像转换为张量，并将像素值归一化到 [0, 1]\n",
    "    transforms.Normalize(              # 对张量进行标准化处理, 提升模型训练的稳定性\n",
    "        mean=[0.485, 0.456, 0.406],    # 使用 ImageNet 数据集的均值\n",
    "        std=[0.229, 0.224, 0.225]      # 使用 ImageNet 数据集的标准差\n",
    "    ),\n",
    "])\n",
    "\n",
    "root_dir=r\"../datasets/PetImages\"\n",
    "\n",
    "\"\"\"\n",
    "datasets.ImageFolder 用于加载图像数据集, 适合用于有文件夹层级结构的分类数据集\n",
    "它会自动根据文件夹结构将数据与标签（类别编号）配对\n",
    "对于如下的文件夹结构：\n",
    "train/\n",
    "    cats/\n",
    "        cat1.jpg\n",
    "        cat2.jpg\n",
    "    dogs/\n",
    "        dog1.jpg\n",
    "        dog2.jpg\n",
    "        \n",
    "ImageFolder 处理之后的结果为：\n",
    "[\n",
    "    (image1_tensor, 0),  # 对应 cats 类别\n",
    "    (image2_tensor, 0),  # 对应 cats 类别\n",
    "    (image3_tensor, 1),  # 对应 dogs 类别\n",
    "    (image4_tensor, 1),  # 对应 dogs 类别\n",
    "]\n",
    "\"\"\"\n",
    "# 加载训练集数据，应用 transforms\n",
    "train_data = datasets.ImageFolder(\n",
    "    os.path.join(root_dir, 'train'),  # 训练数据所在路径\n",
    "    transform                    # 数据增强和预处理\n",
    ")\n",
    "\n",
    "# 加载测试集数据，应用 transforms\n",
    "test_data = datasets.ImageFolder(\n",
    "    os.path.join(root_dir, 'test'),  # 测试数据所在路径\n",
    "    transform                    # 数据增强和预处理\n",
    ")\n",
    "\n",
    "train_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "9641338e-1811-466f-8670-748d1de65899",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "训练集类别数量: 2\n",
      "训练集类别名称: ['Cat', 'Dog']\n",
      "训练集数据数量: 22500\n",
      "训练集第一个样本: torch.Size([3, 150, 150]), 0\n",
      "测试集类别数量: 2\n",
      "测试集类别名称: ['Cat', 'Dog']\n",
      "测试集数据数量: 2500\n",
      "测试集第一个样本: torch.Size([3, 150, 150]), 0\n"
     ]
    }
   ],
   "source": [
    "# 查看数据集的内容\n",
    "print(f\"训练集类别数量: {len(train_data.classes)}\")\n",
    "print(f\"训练集类别名称: {train_data.classes}\")\n",
    "print(f\"训练集数据数量: {len(train_data)}\")\n",
    "print(f\"训练集第一个样本: {train_data[0][0].shape}, {train_data[0][1]}\")  # 打印第一个样本 (image, label)\n",
    "\n",
    "print(f\"测试集类别数量: {len(test_data.classes)}\")\n",
    "print(f\"测试集类别名称: {test_data.classes}\")\n",
    "print(f\"测试集数据数量: {len(test_data)}\")\n",
    "print(f\"测试集第一个样本: {test_data[0][0].shape}, {test_data[0][1]}\")  # 打印第一个样本 (image, label)\n",
    "# 图片形状torch.Size([3, 150, 150] : 150, 150是图片大小， 3代表三个通道(r, g, b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "96624a8e-658b-4bae-b8f5-c5794f9b1666",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 查看数据集图片\n",
    "\n",
    "# 定义函数将 Tensor 转为 numpy 格式的图片\n",
    "def imshow(img, title):\n",
    "    img = img.numpy().transpose((1, 2, 0))  # 将通道维度移到最后 (H, W, C)\n",
    "    img = np.clip(img, 0, 1)  # 确保像素值在 [0, 1] 范围\n",
    "    plt.imshow(img)\n",
    "    plt.title(title)\n",
    "    plt.axis('off')\n",
    "\n",
    "# 获取两张不同类别的图像\n",
    "def get_two_classes_samples(dataset):\n",
    "    \"\"\"\n",
    "    高效获取数据集中每个类别的样本。\n",
    "    \"\"\"\n",
    "    class_indices = {label: None for label in range(len(dataset.classes))}  # 存储每个类别的索引\n",
    "    samples = {}  # 存储样本\n",
    "    half_len = len(dataset) // 2  # 数据集一半的长度\n",
    "    \n",
    "    # 遍历前后索引寻找每个类别的第一个样本\n",
    "    for idx in range(half_len):\n",
    "        # 从前往后找\n",
    "        _, label = dataset[idx]\n",
    "        if class_indices[label] is None:\n",
    "            class_indices[label] = idx  # 记录该类别的第一个样本索引\n",
    "            samples[label] = dataset[idx][0]  # 存储对应的图片数据\n",
    "        \n",
    "        # 从后往前找\n",
    "        _, label = dataset[len(dataset) - 1 - idx]\n",
    "        if class_indices[label] is None:\n",
    "            class_indices[label] = len(dataset) - 1 - idx\n",
    "            samples[label] = dataset[len(dataset) - 1 - idx][0]\n",
    "        \n",
    "        # 如果已找到所有类别，停止搜索\n",
    "        if len(samples) == len(dataset.classes):\n",
    "            break\n",
    "\n",
    "    return samples\n",
    "\n",
    "\n",
    "# 从训练集获取两张不同类别的图像\n",
    "class_samples = get_two_classes_samples(train_data)\n",
    "\n",
    "# 可视化\n",
    "plt.figure(figsize=(10, 5))\n",
    "for i, (label, img) in enumerate(class_samples.items()):\n",
    "    plt.subplot(1, 2, i+1)\n",
    "    imshow(img, f\"Class: {train_data.classes[label]}\")\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "486d81df-7a88-426d-a2a9-a3d6dfe19088",
   "metadata": {},
   "outputs": [],
   "source": [
    "batch_size=32\n",
    "\n",
    "train_loader = DataLoader(train_data, batch_size=batch_size, shuffle=True, pin_memory=True)\n",
    "test_loader = DataLoader(test_data, batch_size=batch_size)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "21b88781-33ad-41d8-b5cc-0fbda715772c",
   "metadata": {},
   "source": [
    "### 模型构建\n",
    "\n",
    "`nn.Conv2d(in_channels, out_channels, kernel_size, stride, padding)`\n",
    "\n",
    "卷积层输出计算公式：\n",
    "$N = (W - F + 2P) / S + 1$\n",
    "其中，\n",
    "- N：输出大小\n",
    "- W：输入大小\n",
    "- F：卷积核大小\n",
    "- P：填充值大小\n",
    "- S：步长大小"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e2ef1a4f-d87f-4e86-95ce-76add5cc4aab",
   "metadata": {},
   "outputs": [],
   "source": [
    "class Net(torch.nn.Module):\n",
    "    def __init__(self):\n",
    "        # 输入形状: (batch, 3, 150, 150)\n",
    "        super(Net, self).__init__()\n",
    "\n",
    "        self.conv1 = torch.nn.Sequential(\n",
    "            torch.nn.Conv2d(3, 32, kernel_size=3, stride=1, padding=1),  # 输出: (batch, 32, 150, 150)\n",
    "            torch.nn.BatchNorm2d(32),  # 增加 Batch Normalization 提高训练稳定性\n",
    "            torch.nn.ReLU(),\n",
    "            torch.nn.MaxPool2d(kernel_size=2, stride=2)  # 输出: (batch, 32, 75, 75)\n",
    "        )\n",
    "\n",
    "        self.conv2 = torch.nn.Sequential(\n",
    "            torch.nn.Conv2d(32, 64, kernel_size=3, stride=1, padding=1),  # 输出: (batch, 64, 75, 75)\n",
    "            torch.nn.BatchNorm2d(64),\n",
    "            torch.nn.ReLU(),\n",
    "            torch.nn.MaxPool2d(kernel_size=2, stride=2)  # 输出: (batch, 64, 37, 37)\n",
    "        )\n",
    "\n",
    "        self.conv3 = torch.nn.Sequential(\n",
    "            torch.nn.Conv2d(64, 128, kernel_size=3, stride=1, padding=1),  # 输出: (batch, 128, 37, 37)\n",
    "            torch.nn.BatchNorm2d(128),\n",
    "            torch.nn.ReLU(),\n",
    "            torch.nn.MaxPool2d(kernel_size=2, stride=2)  # 输出: (batch, 128, 18, 18)\n",
    "        )\n",
    "        self.conv4 = torch.nn.Sequential(\n",
    "            torch.nn.Conv2d(128, 256, kernel_size=3, stride=1, padding=1),  # 输出: (batch, 256, 18, 18)\n",
    "            torch.nn.BatchNorm2d(256),\n",
    "            torch.nn.ReLU(),\n",
    "            torch.nn.MaxPool2d(kernel_size=2, stride=2)  # 输出: (batch, 256, 9, 9)\n",
    "        )\n",
    "\n",
    "        self.fc = torch.nn.Sequential(\n",
    "            torch.nn.Linear(256 * 9 * 9, 512),  # 全连接层, 输入: 128*18*18\n",
    "            torch.nn.ReLU(),\n",
    "            torch.nn.Dropout(0.5),  # 添加 Dropout 防止过拟合\n",
    "            torch.nn.Linear(512, 2)  # 最后输出 2 个类别\n",
    "        )\n",
    "\n",
    "    def forward(self, x):\n",
    "        batch_size = x.size(0)\n",
    "        x = self.conv1(x)  # 第一卷积模块\n",
    "        x = self.conv2(x)  # 第二卷积模块\n",
    "        x = self.conv3(x)  # 第三卷积模块\n",
    "        x = self.conv4(x)  # 第四卷积模块\n",
    "        x = x.view(batch_size, -1)  # 展平为全连接层输入\n",
    "        x = self.fc(x)  # 全连接层\n",
    "        return x  # 输出形状: (batch, 2)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "17534800-b9ee-4066-a956-c465117c1f08",
   "metadata": {},
   "outputs": [],
   "source": [
    "model=Net().to(device)\n",
    "\n",
    "lr=1e-3\n",
    "\n",
    "# 定义损失函数和优化器\n",
    "loss_fn = torch.nn.CrossEntropyLoss()  # 适用于多（二）分类问题, 适合模型输出为 (batch_size, class_num)\n",
    "\"\"\"\n",
    "权重衰减 (L2正则化)\n",
    "在优化器中加入权重衰减，可以防止过拟合\n",
    "大的权重值会导致模型对训练数据的拟合过度，从而丧失对新数据的泛化能力。\n",
    "L2 正则化通过惩罚大权重值，迫使模型学到更平滑的特征分布，而不是依赖特定特征的绝对大小。\n",
    "\"\"\"\n",
    "optimizer = torch.optim.Adam(model.parameters(), lr=1e-4, weight_decay=1e-4) # 使用 Adam 优化器\n",
    "# optimizer = torch.optim.Adam(model.parameters(), lr=1e-4)\n",
    "\"\"\"\n",
    "学习率衰减策略\n",
    "StepLR: 每隔一定的步数衰减学习率\n",
    "它会在每 step_size 个 epoch 后将学习率乘以一个因子 gamma\n",
    "这种方法可以在模型训练进入瓶颈时，减少学习率，从而让优化器更精细地调整权重，避免震荡或错过局部最优\n",
    "\"\"\"\n",
    "scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=10, gamma=0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "c176546a-0150-4278-a70f-37e1e6d92a22",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Net(\n",
      "  (conv1): Sequential(\n",
      "    (0): Conv2d(3, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "    (2): ReLU()\n",
      "    (3): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "  )\n",
      "  (conv2): Sequential(\n",
      "    (0): Conv2d(32, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "    (2): ReLU()\n",
      "    (3): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "  )\n",
      "  (conv3): Sequential(\n",
      "    (0): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "    (2): ReLU()\n",
      "    (3): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "  )\n",
      "  (conv4): Sequential(\n",
      "    (0): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "    (2): ReLU()\n",
      "    (3): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "  )\n",
      "  (fc): Sequential(\n",
      "    (0): Linear(in_features=20736, out_features=512, bias=True)\n",
      "    (1): ReLU()\n",
      "    (2): Dropout(p=0.5, inplace=False)\n",
      "    (3): Linear(in_features=512, out_features=2, bias=True)\n",
      "  )\n",
      ")\n",
      "模型总参数数量: 11,007,746\n",
      "模型可训练参数数量: 11,007,746\n"
     ]
    }
   ],
   "source": [
    "# 查看模型结构\n",
    "# 打印模型参数总数和可训练参数总数\n",
    "def count_parameters(model):\n",
    "    total_params = sum(p.numel() for p in model.parameters())  # 所有参数数量\n",
    "    trainable_params = sum(p.numel() for p in model.parameters() if p.requires_grad)  # 需要训练的参数数量\n",
    "    print(f\"模型总参数数量: {total_params:,}\")\n",
    "    print(f\"模型可训练参数数量: {trainable_params:,}\")\n",
    "\n",
    "print(model)\n",
    "count_parameters(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "e5850833-7228-4f04-ae20-a289b7a8c9d6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 训练函数\n",
    "def train(dataloader, model, loss_fn, optimizer):\n",
    "    model.train()  # 设置模型为训练模式\n",
    "    running_loss = 0.0\n",
    "    correct = 0\n",
    "    total = 0\n",
    "\n",
    "    # 使用 tqdm 包裹数据加载器，显示进度条 (因为训练过程会比较慢)\n",
    "    progress_bar = tqdm(dataloader, desc=\"Training\", leave=False)\n",
    "    for images, labels in progress_bar:\n",
    "        # 将数据移动到设备\n",
    "        images, labels = images.to(device), labels.to(device)\n",
    "\n",
    "        # 前向传播\n",
    "        outputs = model(images)\n",
    "        loss = loss_fn(outputs, labels)\n",
    "\n",
    "        # 反向传播和优化\n",
    "        optimizer.zero_grad()\n",
    "        loss.backward()\n",
    "        optimizer.step()  # 更新模型参数\n",
    "\n",
    "        # 统计指标\n",
    "        running_loss += loss.item()\n",
    "        _, predicted = torch.max(outputs, 1)\n",
    "        total += labels.size(0)\n",
    "        correct += (predicted == labels).sum().item()\n",
    "\n",
    "        # 更新进度条描述\n",
    "        progress_bar.set_postfix(loss=loss.item())\n",
    "        \n",
    "    scheduler.step()  # 更新学习率\n",
    "\n",
    "    accuracy = 100 * correct / total\n",
    "    avg_loss = running_loss / len(dataloader)\n",
    "    return avg_loss, accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "94df5294-4bc0-43d9-8fcc-c723e35e8231",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 测试函数\n",
    "def evaluate(dataloader, model, loss_fn):\n",
    "    model.eval()  # 设置模型为评估模式\n",
    "    running_loss = 0.0\n",
    "    correct = 0\n",
    "    total = 0\n",
    "\n",
    "    with torch.no_grad():  # 关闭梯度计算\n",
    "        progress_bar = tqdm(dataloader, desc=\"Evaluating\", leave=False)\n",
    "        for images, labels in dataloader:\n",
    "            # 将数据移动到设备\n",
    "            images, labels = images.to(device), labels.to(device)\n",
    "\n",
    "            # 前向传播\n",
    "            outputs = model(images)\n",
    "            loss = loss_fn(outputs, labels)\n",
    "\n",
    "            # 统计指标\n",
    "            running_loss += loss.item()\n",
    "            _, predicted = torch.max(outputs, 1)\n",
    "            total += labels.size(0)\n",
    "            correct += (predicted == labels).sum().item()\n",
    "            # 更新进度条描述\n",
    "            progress_bar.set_postfix(loss=loss.item())\n",
    "\n",
    "    accuracy = 100 * correct / total\n",
    "    avg_loss = running_loss / len(dataloader)\n",
    "    return avg_loss, accuracy"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "776bace7-71c5-4b94-b098-c46f031de9ca",
   "metadata": {},
   "source": [
    "### 模型训练"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "43f96cab-f89b-4391-987d-54a520ed83a3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training:  99%|█████████████████████████████████████████████████████████▏| 694/704 [13:44<00:11,  1.12s/it, loss=0.643]D:\\develop\\anaconda\\envs\\pytorch\\lib\\site-packages\\PIL\\TiffImagePlugin.py:900: UserWarning: Truncated File Read\n",
      "  warnings.warn(str(msg))\n",
      "                                                                                                                       \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 1, Train_acc:71.7%, Train_loss:0.556, Test_acc:80.4%，Test_loss:0.439\n",
      "Epoch 2/20\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                                                       \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 2, Train_acc:80.2%, Train_loss:0.432, Test_acc:79.5%，Test_loss:0.433\n",
      "Epoch 3/20\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                                                       \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 3, Train_acc:83.0%, Train_loss:0.381, Test_acc:83.0%，Test_loss:0.371\n",
      "Epoch 4/20\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                                                       \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 4, Train_acc:84.7%, Train_loss:0.348, Test_acc:83.8%，Test_loss:0.365\n",
      "Epoch 5/20\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                                                       \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 5, Train_acc:86.0%, Train_loss:0.322, Test_acc:86.1%，Test_loss:0.318\n",
      "Epoch 6/20\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                                                       \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 6, Train_acc:87.1%, Train_loss:0.303, Test_acc:85.6%，Test_loss:0.337\n",
      "Epoch 7/20\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                                                       \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 7, Train_acc:88.0%, Train_loss:0.283, Test_acc:87.9%，Test_loss:0.287\n",
      "Epoch 8/20\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                                                       \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 8, Train_acc:88.7%, Train_loss:0.267, Test_acc:87.4%，Test_loss:0.291\n",
      "Epoch 9/20\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                                                       \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 9, Train_acc:89.6%, Train_loss:0.252, Test_acc:90.0%，Test_loss:0.246\n",
      "Epoch 10/20\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                                                       \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch:10, Train_acc:90.1%, Train_loss:0.244, Test_acc:87.2%，Test_loss:0.308\n",
      "Epoch 11/20\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                                                       \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch:11, Train_acc:92.0%, Train_loss:0.197, Test_acc:91.9%，Test_loss:0.200\n",
      "Epoch 12/20\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                                                       \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch:12, Train_acc:92.6%, Train_loss:0.186, Test_acc:91.5%，Test_loss:0.208\n",
      "Epoch 13/20\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                                                       \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch:13, Train_acc:92.8%, Train_loss:0.181, Test_acc:92.4%，Test_loss:0.195\n",
      "Epoch 14/20\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                                                       \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch:14, Train_acc:92.9%, Train_loss:0.181, Test_acc:91.2%，Test_loss:0.214\n",
      "Epoch 15/20\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                                                       \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch:15, Train_acc:93.0%, Train_loss:0.173, Test_acc:92.5%，Test_loss:0.189\n",
      "Epoch 16/20\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                                                       \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch:16, Train_acc:93.2%, Train_loss:0.173, Test_acc:92.0%，Test_loss:0.190\n",
      "Epoch 17/20\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                                                       \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch:17, Train_acc:93.1%, Train_loss:0.170, Test_acc:92.3%，Test_loss:0.187\n",
      "Epoch 18/20\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                                                       \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch:18, Train_acc:93.4%, Train_loss:0.166, Test_acc:92.8%，Test_loss:0.186\n",
      "Epoch 19/20\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                                                       \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch:19, Train_acc:93.5%, Train_loss:0.166, Test_acc:92.4%，Test_loss:0.187\n",
      "Epoch 20/20\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                                                       "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch:20, Train_acc:93.6%, Train_loss:0.166, Test_acc:92.5%，Test_loss:0.185\n",
      "训练完成!\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r"
     ]
    }
   ],
   "source": [
    "# 开始训练\n",
    "\"\"\"\n",
    "如果不想训练太久可以适当减小epoch\n",
    "epoch=10的时候训练正确率就达到了77.0%, epoch=30时才为81.0% \n",
    "再增加epoch训练效果增加不明显, 但是结果明显还未收敛。\n",
    "为了更好的效果作者就增大了epoch\n",
    "\"\"\"\n",
    "num_epochs = 20\n",
    "\n",
    "train_loss = []\n",
    "train_acc  = []\n",
    "test_loss  = []\n",
    "test_acc   = []\n",
    "\n",
    "for epoch in range(num_epochs):\n",
    "    print(f\"Epoch {epoch+1}/{num_epochs}\")\n",
    "    \n",
    "    epoch_train_loss, epoch_train_acc = train(train_loader, model, loss_fn, optimizer)\n",
    "\n",
    "    # 在测试集上评估\n",
    "    epoch_test_loss, epoch_test_acc = evaluate(test_loader, model, loss_fn)\n",
    "\n",
    "    train_acc.append(epoch_train_acc)\n",
    "    train_loss.append(epoch_train_loss)\n",
    "    test_acc.append(epoch_test_acc)\n",
    "    test_loss.append(epoch_test_loss)\n",
    "    # 打印训练和测试结果\n",
    "    template = ('Epoch:{:2d}, Train_acc:{:.1f}%, Train_loss:{:.3f}, Test_acc:{:.1f}%，Test_loss:{:.3f}')\n",
    "    print(template.format(epoch+1, epoch_train_acc, epoch_train_loss, epoch_test_acc, epoch_test_loss))\n",
    "\n",
    "print(\"训练完成!\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5565e316-1967-41a0-810c-aa17f2809ed9",
   "metadata": {},
   "source": [
    "### 结果可视化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "ca6ed2f2-317a-4118-8707-16ecd7b2f5f5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x300 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "epochs_range = range(num_epochs)\n",
    "\n",
    "plt.figure(figsize=(12, 3))\n",
    "plt.subplot(1, 2, 1)\n",
    "\n",
    "plt.plot(epochs_range, train_acc, label='Training Accuracy')\n",
    "plt.plot(epochs_range, test_acc, label='Test Accuracy')\n",
    "plt.legend(loc='lower right')\n",
    "plt.title('Training and Validation Accuracy')\n",
    "\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.plot(epochs_range, train_loss, label='Training Loss')\n",
    "plt.plot(epochs_range, test_loss, label='Test Loss')\n",
    "plt.legend(loc='upper right')\n",
    "plt.title('Training and Validation Loss')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c34c64fa-9e5d-4bd2-a4ce-59630b82f5e7",
   "metadata": {},
   "source": [
    "### 模型保存"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "69db0aa7-3203-4d76-a84e-2a36844867d2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 指定保存路径\n",
    "save_dir = '../models/2_Cat_Dog_Image_Classification'\n",
    "\n",
    "# 确保目录存在，如果不存在则创建\n",
    "import os\n",
    "if not os.path.exists(save_dir):\n",
    "    os.makedirs(save_dir)\n",
    "\n",
    "# 保存模型\n",
    "torch.save(model.state_dict(), os.path.join(save_dir, 'model_weights.pth'))\n",
    "\n",
    "# # 加载模型参数\n",
    "# model.load(torch.load(os.path.join(save_dir, 'model_weights.pth')))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4f1bd51d-6ef7-4f6c-8552-e475b168e544",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.20"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
